Data to 7 Oct 2026

Deep Learning Techniques for Inverse Problems in Imaging

arXiv 2005.06001 ↗First posted 12 May 2020eess.IV cs.LG stat.MLcited by 290

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Cited by 46 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the trade-offs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.

Key facts. Cited by 290 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.

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Citing papers per year: 2021: 50; 2022: 59; 2023: 64; 2024: 43; 2025: 41; 2026: 33.

290 papers.

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2022Denoising Diffusion Restoration Modelscited by 951
2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
2021Deblurring via Stochastic Refinementcited by 248
2022Transformers in Medical Imaging: A Surveycited by 245
2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
2024A Survey on Diffusion Models for Inverse Problemscited by 173
2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 164
2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 156
2023Looped Transformers are Better at Learning Learning Algorithmscited by 135
2021Deep Equilibrium Architectures for Inverse Problems in Imagingcited by 126
2021Intermediate Layer Optimization for Inverse Problems using Deep Generative Modelscited by 70
2023Prompt-tuning latent diffusion models for inverse problemscited by 70

38 more

2024DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Modelscited by 60
2022Generative Joint Source-Channel Coding for Semantic Image Transmissioncited by 55
2021Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problemcited by 53
2024Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matchingcited by 50
2021Early Stopping for Deep Image Priorcited by 44
2021Instance-Optimal Compressed Sensing via Posterior Samplingcited by 43
2022A Neural-Network-Based Convex Regularizer for Inverse Problemscited by 40
2022Imaging with Equivariant Deep Learningcited by 35
2021Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpointcited by 32
2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
2021Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priorscited by 29
2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
2021Learning the optimal Tikhonov regularizer for inverse problemscited by 26
2023What's in a Prior? Learned Proximal Networks for Inverse Problemscited by 24
2023ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretrainingcited by 24
2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
2021Hyperparameter Tuning is All You Need for LISTAcited by 22
2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
2022Theoretical Perspectives on Deep Learning Methods in Inverse Problemscited by 22
2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 19
2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
2024FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Modelcited by 18
2022Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Datacited by 17
2023Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problemscited by 17
2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
2023Manifold Learning by Mixture Models of VAEs for Inverse Problemscited by 16
2021Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrologycited by 15
2021Self-Validation: Early Stopping for Single-Instance Deep Generative Priorscited by 15
2021Joint inference and input optimization in equilibrium networkscited by 15
2022The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problemscited by 15
2023Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstructioncited by 15
2021Provable Compressed Sensing with Generative Priors via Langevin Dynamicscited by 14
2021Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priorscited by 14
2023DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistencycited by 14
2023Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurringcited by 14
2021Conditional Variational Autoencoder for Learned Image Reconstructioncited by 13
2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13

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